Search for: All records

Sensitive numbers play an unparalleled role in identification and authentication. Recent research has revealed plenty of side-channel attacks to infer keystrokes, which require either a training phase or a dictionary to build the relationship between an observed signal disturbance and a keystroke. However, training-based methods are unpractical as the training data about the victim are hard to obtain, while dictionary-based methods cannot infer numbers, which are not combined according to linguistic rules like letters are. We observe that typing a number creates not only a number of observed disturbances in space (each corresponding to a digit), but also a sequence of periods between each disturbance. Based upon existing work that utilizes inter-keystroke timing to infer keystrokes, we build a novel technique called WINK that combines the spatial and time domain information into a spatiotemporal feature of keystroke-disturbed wireless signals. With this spatiotemporal feature, WINK can infer typed numbers without the aid of any training. Experimental results on top of software-defined radio platforms show that WINK can vastly reduce the guesses required for breaking certain 6-digit PINs from 1 million to as low as 16, and can infer over 52% of user-chosen 6-digit PINs with less than 100 attempts. 

Low-cost and easily obtained Global Navigation Satellite System (e.g., GPS) receivers are broadly embedded into various devices for providing location information. In this work, we develop a secret key establishment by utilizing the driving data obtained from GPS. Those data may exhibit randomness as the driver may alternatively step on the accelerator and brake pedals from time to time with varying force in order to adapt to the road traffic during driving. A driving vehicle provides a physically secure boundary as the devices co-located within the vehicle can observe common GPS data, as opposed to devices that do not experience the trip. We implement this key establishment in a real-world environment on top of off-the-shelf GPS-equipped devices as well as widely deployed GPS modules each connected with Raspberry Pi. Extensive experimental results show that when a user drives around 1.36 km for 1.32 minutes on average under moderate traffic conditions, two legitimate GPS-equipped devices in the vehicle can successfully establish a 128-bit secret key. Meanwhile, an attacker following the target vehicle is unable to establish a secret key with the legitimate devices. 

With the advent of the in-vehicle infotainment (IVI) systems (e.g., Android Automotive) and other portable devices (e.g., smartphones) that may be brought into a vehicle, it becomes crucial to establish a secure channel between the vehicle and an in-vehicle device or between two in-vehicle devices. Traditional pairing schemes are tedious, as they require user interaction (e.g., manually typing in a passcode or bringing the two devices close to each other). Modern vehicles, together with smartphones and many emerging Internet-of-things (IoT) devices (e.g., dashcam) are often equipped with built-in Global Positioning System (GPS) receivers. In this paper, we propose a GPS-based Key estab- lishment technique, called GPSKey, by leveraging the inherent randomness of vehicle movement. Specifically, vehicle movement changes with road ground conditions, traffic situations, and pedal operations. It thus may have rich randomness. Meanwhile, two in- vehicle GPS receivers can observe the same vehicle movement and exploit it for key establishment without requiring user interaction. We implement a prototype of GPSKey on top of off-the-shelf devices. Experimental results show that legitimate devices in the same vehicle require 1.18-minute of driving on average to establish a 128-bit key. Meanwhile, the attacker who follows or leads the victim’s vehicle is unable to infer the key. 

Existing research work has identified a new class of attacks that can eavesdrop on the keystrokes in a non-invasive way without infecting the target computer to install malware. The common idea is that pressing a key of a keyboard can cause a unique and subtle environmental change, which can be captured and analyzed by the eavesdropper to learn the keystrokes. For these attacks, however, a training phase must be accomplished to establish the relationship between an observed environmental change and the action of pressing a specific key. This significantly limits the impact and practicality of these attacks. In this paper, we discover that it is possible to design keystroke eavesdropping attacks without requiring the training phase. We create this attack based on the channel state information extracted from the wireless signal. To eavesdrop on keystrokes, we establish a mapping between typing each letter and its respective environmental change by exploiting the correlation among observed changes and known structures of dictionary words. To defend against this attack, we propose a reactive jamming mechanism that launches the jamming only during the typing period. Experimental results on software-defined radio platforms validate the impact of the attack and the performance of the defense. 

Automatic Speech Recognition (ASR) systems are widely used in various online transcription services and personal digital assistants. Emerging lines of research have demonstrated that ASR systems are vulnerable to hidden voice commands, i.e., audio that can be recognized by ASRs but not by humans. Such attacks, however, often either highly depend on white-box knowledge of a specific machine learning model or require special hardware to construct the adversarial audio. This paper proposes a new model-agnostic and easily-constructed attack, called CommanderGabble, which uses fast speech to camouflage voice commands. Both humans and ASR systems often misinterpret fast speech, and such misinterpretation can be exploited to launch hidden voice command attacks. Specifically, by carefully manipulating the phonetic structure of a target voice command, ASRs can be caused to derive a hidden meaning from the manipulated, high-speed version. We implement the discovered attacks both over-the-wire and over-the-air, and conduct a suite of experiments to demonstrate their efficacy against 7 practical ASR systems. Our experimental results show that the over-the-wire attacks can disguise as many as 96 out of 100 tested voice commands into adversarial ones, and that the over-the-air attacks are consistently successful for all 18 chosen commands in multiple real-world scenarios.